SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 6351–6375 of 15113 papers

TitleStatusHype
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
Human-centered mechanism design with Democratic AI—0
Generative Adversarial Exploration for Reinforcement Learning—0
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence—0
Quantile-Based Policy Optimization for Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization—0
moolib: A Platform for Distributed RLCode2
Probe-Based Interventions for Modifying Agent Behavior—0
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Hyperparameter Tuning for Deep Reinforcement Learning Applications—0
Using Deep Reinforcement Learning for Zero Defect Smart Forging—0
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching—0
MOORe: Model-based Offline-to-Online Reinforcement Learning—0
The Paradox of Choice: Using Attention in Hierarchical Reinforcement LearningCode1
Pearl: Parallel Evolutionary and Reinforcement Learning LibraryCode1
Generative Planning for Temporally Coordinated Exploration in Reinforcement LearningCode0
Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning—0
Constrained Policy Optimization via Bayesian World ModelsCode1
Large-Scale Graph Reinforcement Learning in Wireless Control Systems—0
State-Conditioned Adversarial Subgoal Generation—0
Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement LearningCode0
Online Attentive Kernel-Based Temporal Difference Learning—0
Multi-Agent Adversarial Attacks for Multi-Channel Communications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified